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Advanced Extremely Efficient Detection of Replica Nodes in Mobile Wireless Sensor Networks Mehdi Safari; Elham Bahmani; Mojtaba Jamshidi; Abdusalam Shaltooki
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Society of Visual Informatics

Today, wireless sensor networks (WSNs) are widely used in many applications including the environment, military, and explorations. One of the most dangerous attacks against these networks is node replication. In this attack, the adversary captures a legal node of the network, generates several copies of the node (called, replica nodes) and injects them in the network. Various algorithms have been proposed to handle replica nodes in stationary and mobile WSNs. One of the most well-known algorithms to handle this attack in mobile WSNs is eXtremely Efficient Detection (XED). The main idea of XED is to generate and exchange random numbers among neighboring nodes. The XED has some drawbacks including high communication and memory overheads and low speed in the detection of replica nodes. In this paper, an algorithm is presented to improve XED. The proposed algorithm is called Advanced XED (AXED) in which each node observes a few numbers of nodes and whenever two nodes meet, a new random number is generated and exchanged. The efficiency of the proposed algorithm is evaluated in terms of the memory and communication overheads and its results are compared with existing algorithms. The comparison results show that the proposed algorithm imposes lower overheads to the nodes. In addition, the proposed algorithm is simulated and the simulation results show that the proposed algorithm is able to detect replica nodes faster than XED.

Using One-hop and Two-hop Neighbouring Information to Defend Against Sybil Attacks in Stationary Wireless Sensor Network Elham Bahmani; Sheida Dashtevan; Abdusalam Shaltooki; Mojtaba Jamshidi
JOIV : International Journal on Informatics Visualization Vol 3, No 2 (2019)
Publisher : Politeknik Negeri Padang

Considering the application of wireless sensor networks (WSNs) in critical areas like war fields, establishing security in these networks is of great challenge. One of the important and dangerous attacks in these networks is the Sybil attack. In this attack, a malicious node broadcasts several IDs simultaneously. Thus, the malicious node of the adversary attracts high traffic to itself and disrupts routing protocols and affects other operations of the network like data aggregation, voting, and resource allocation, negatively. In this paper, an efficient algorithm based on one-hop and two-hop neighborhood information is proposed to detect Sybil nodes in the stationary WSNs. The proposed algorithm is executed locally with the collaboration of neighboring nodes. The main purpose of the proposed algorithm is to increase the accuracy of detecting Sybil nodes under various conditions including the condition in which a malicious node broadcasts a few numbers of Sybil IDs which is the shortcoming of most existing algorithms. The proposed algorithm is simulated in MATLAB and its efficiency is compared with two similar algorithms in terms of true and false detection rates. The proposed algorithm not only reduces communication overhead but also increases the accuracy of detecting Sybil nodes compared to two similar algorithms.

A Multi-Criteria Ranking Algorithm Based on the VIKOR Method for Meta-Search Engines Mojtaba Jamshidi; Mastoreh Haji; Mohamad Reza Kamankesh; Mahya Daghineh; Abdusalam Abdulla Shaltooki
JOIV : International Journal on Informatics Visualization Vol 3, No 3 (2019)
Publisher : Society of Visual Informatics

Ranking of web pages is one of the most important parts of search engines and is, in fact, a process that through it the quality of a page is estimated by the search engine. In this study, a ranking algorithm based on VIKOR multi-criteria decision-making method for Meta-Search Engines (MSEs) was proposed. In this research, the considered MSE first will receive the suggested pages associated with the search term from eight search engines including, Teoma, Google, Yahoo!, AlltheWeb, AltaVista, Wisenut, ODP, MSN. The results, at most 10 first pages are selected from each search engine and creates the initial dataset contains 80 web pages. The proposed parser is then executed on these pages and the eight criteria including the rank of web page in the related search engine, access time, number of repetitions of search terms, positions of search term at the webpage, numbers of media at the webpage, the number of imports in the webpage, the number of incoming links, and the number of outgoing links are extracted from these web pages. Finally, by using the VIKOR method and these extracted criteria, web pages will rank and 10 top results will be provided for the user. To implement the proposed method, JAVA and MATLAB languages are used. In the experiments, the proposed method is implemented for a query and its ranking results have been compared in terms of accuracy with three famous search engine including Google, Yahoo, and MSN. The results of comparisons show that the proposed method offers higher accuracy.

A Dynamic ID Assignment Mechanism to Defend Against Node Replication Attack in Static Wireless Sensor Networks Mojtaba Jamshidi; Abdusalam Abdulla Shaltooki; Zahra Dagal Zadeh; Aso Mohammad Darwesh
JOIV : International Journal on Informatics Visualization Vol 3, No 1 (2019)
Publisher : Politeknik Negeri Padang

One of the known dangerous attacks against wireless sensor networks (WSNs) is node replica. In this attack, adversary captures one or more normal nodes of the network, generates copies of them (replicas) and deploy them in the network. These copied nodes are controlled by the adversary which can establish a shared key with other nodes of the network easily and exchange information. In this paper, a novel algorithm is proposed to defend against this attack in static sensor networks. The proposed algorithm employs a multi-tree architecture to assign ID to the nodes dynamically and prevent attachment of the injected replica nodes to the network by the adversary. The efficiency of the proposed algorithm is evaluated in terms of memory, communication, and computation overheads and the results are compared with other existing algorithms. Comparison results indicate the superiority of the proposed algorithm in terms of mentioned measures. In addition, the proposed algorithm is simulated and its efficiency is evaluated in terms of probability of detecting replica nodes. Experiment results show that the proposed algorithm has favorable performance in detection of replica nodes.

The Use of Data Mining Techniques in Predicting the Noise Emitted By the Trailing Edge of Aerodynamic Objects Abdusalam Shaltooki; Mojtaba Jamshidi
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Aerodynamic is a branch of fluid dynamics that evaluates the behavior of airflow and its interaction with moving objects. The most important application of aerodynamic is in aerospace engineering, designing and construction of flying objects. Reduction of noise emitted by aerodynamic objects is one of the most important challenges in this area and many efforts have been to reduce its negative effects. The prediction of noise emitted from these aerodynamic objects is a low-cost and fast approach that can partially replace the "fabrication and testing" phase. One of the most common and successful tools in prediction procedures is data mining technology. In this paper, the performance of different data mining algorithms such as Random Forest, J48, RBF Network, SVM, MLP, Logistic, and Bagging is evaluated in predicting the amount of noise emitted from aerodynamic objects. The experiments are conducted on a dataset collected by NASA, which is called "Airfoil Self-Noise". The obtained results illustrate that the proposed hybrid model derived from the combination of Random Forest and Bagging algorithms has better performance compared to other methods with an accuracy of 77.6% and mean absolute error of 0.2279.

Breast Cancer Prediction Using a Hybrid Data Mining Model Elham Bahmani; Mojtaba Jamshidi; Abdusalam Shaltooki
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Society of Visual Informatics

Today, with the emergence of data mining technology and access to useful data, valuable information in different areas can be explored. Data mining uses machine learning algorithms to extract useful relationships and knowledge from a large amount of data and offers an automatic tool for various predictions and classifications. One of the most common applications of data mining in medicine and health-care is to predict different types of breast cancer which has attracted the attention of many scientists. In this paper, a hybrid model employing three algorithms of Naive Bayes Network, RBF Network, and K-means clustering is presented to predict breast cancer type. In the proposed model, the voting approach is used to combine the results obtained from the above three algorithms. Dataset used in this study is called Breast Cancer Wisconsin taken from data sources of UCI. The proposed model is implemented in MATLAB and its efficiency in predicting breast cancer type is evaluated on Breast Cancer Wisconsin dataset. Results show that the proposed hybrid model achieves an accuracy of 99% and mean absolute error of 0.019 which is superior over other models.

A Hybrid Key Pre-Distribution Scheme for Securing Communications in Wireless Sensor Networks Mojtaba Jamshidi; Hamid Bazargan; Abdusalam Abdulla Shaltooki; Aso Mohammad Darwesh
JOIV : International Journal on Informatics Visualization Vol 3, No 1 (2019)
Publisher : Society of Visual Informatics

Wireless Sensor Network (WSN) is a type of ad hoc networks which consist of hundreds to thousands of sensor nodes. These sensor nodes collaborate to surveillance environment. WSNs have a variety of applications in military, industrial and other fields and they are fit to study environments that presence of human being is costly or dangerous. Sensor nodes have memory, energy and processing limitations. According to sensors' limitations and also increasing use of these networks in military fields, establishing a secure WSN is very important and challenging. Applying Key Predistribution Schemes (KPSs) is one of the effective and useful mechanisms to provide security in WSN. In this paper, a hybrid KPS is proposed that support three various keys, primary pairwise, polynomial, and ordinary. The proposed scheme has been implemented using J-SIM simulator and its performance has been evaluated in terms of maximum supportable network sizes and resiliency against node capture attack, by performing some experiments. Simulation results have been compared with Basic, q-Composite, RS, Cluster-Based, QS, and Double-Key Hash schemes. The compared results showed that the proposed scheme has a better resiliency against links disclosing via enemies.

Sybil Node Detection in Mobile Wireless Sensor Networks Using Observer Nodes Mojtaba Jamshidi; Milad Ranjbari; Mehdi Esnaashari; Nooruldeen Nasih Qader; Mohammad Reza Meybodi
JOIV : International Journal on Informatics Visualization Vol 2, No 3 (2018)
Publisher : Society of Visual Informatics

Sybil attack is one of the well-known dangerous attacks against wireless sensor networks in which a malicious node attempts to propagate several fabricated identities. This attack significantly affects routing protocols and many network operations, including voting and data aggregation. The mobility of nodes in mobile wireless sensor networks makes it problematic to employ proposed Sybil node detection algorithms in static wireless sensor networks, including node positioning, RSSI-based, and neighbour cooperative algorithms. This paper proposes a dynamic, light-weight, and efficient algorithm to detect Sybil nodes in mobile wireless sensor networks. In the proposed algorithm, observer nodes exploit neighbouring information during different time periods to detect Sybil nodes. The proposed algorithm is implemented by J-SIM simulator and its performance is compared with other existing algorithm by conducting a set of experiments. Simulation results indicate that the proposed algorithm outperforms other existing methods regarding detection rate and false detection rate. Moreover, they also showed that the mean detection rate and false detection rate of the proposed algorithm are respectively 99% and less than 2%.

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